Author: DWGtalks

Step 1: Be Crawlable

This may even sound simple, but this is a vital preliminary process. Your best bet for getting seen in large language models is to enable them to crawl your site. There are many LLM crawlers, such as OpenAI and Anthropic.

Specific crawlers are likely to act so predatorily that they can provoke scraping and DDoS mitigation actions. If you have automated blocks against aggressive bots, align with your IT team to make sure that you do not block the LLM crawlers that you do want to access.

When you deploy a CDN, such as Fastly or Cloudflare, your LLM crawlers should not be blocked by default.

Step 2: Continue Gaining Search Engine Optimization Rankings

The best strategy for GEO is relatively simple: target traditional SEO. Rank highly on Google (Gemini and AI Overviews), Bing (ChatGPT and Copilot), Brave (Claude), and Baidu (DeepSeek).

SEO remains crucial in Google Search and other search engines. However, the introduction of AI search with its advanced models is changing the game of generating and providing search results by eliminating the need to match keywords merely and instead offering highly context-sensitive and personalized search results.

Step 3: Target the Query Fanout

The current generation of LLMs does a little more than simple RAG. They generate multiple queries. This is called query fanout. LLMs analyze user queries and process entire sequences of text, allowing them to generate AI-generated responses that are tailored to user intent.

As an example, I also recently asked ChatGPT, “What is the newest Google patent that SEOs talk about?”, and it did two web searches on the queries “latest Google patent discussed by SEOs patent 2025 SEO forum” and “latest Google patent SEOs 2025 discussed” (which returned identical results as the former query).

Tips: Check typical query fanouts to your prompts and strive to acquire ranking on those keywords too.

The most common fanout patterns I encounter in ChatGPT involve using the word “forums” to inquire about a topic in the public and attaching “interview” to questions about people. Also often mentioned is the current year (2025).

Caution: fanout patterns can change across LLMs and may change over time as well. What we are seeing today may not be relevant in a year.

Step 4: Keep Consistency Across Your Brand Mentions

It is a rather basic but essential exercise for all people, whether in a business context or otherwise. Ensure your presence remains consistent across all platforms, including X, LinkedIn, your web page, Crunchbase, and GitHub. Always maintain the same profile description, regardless of where you are.

When listing your occupation on multiple social sites, such as “GEO consultant on small business,” do not alter it to “AIO expert” on Github or “LLMO Freelancer” in your press releases.

On ChatGPT and Google AI Overviews, people have claimed to see positive results within a couple of days by simply utilizing a consistent self-description throughout the web. The same can be said about PR coverage, too. The broader and better the PR coverage you get on your brand, the more you will get referred back to users by the large language models.

Step 5: Avoid JavaScript

As an SEO, I require a minimum amount of JavaScript to be utilized. I insist on it as a GEO!

The majority of LLM crawlers are unable to process JavaScript. You are out if your main content is buried in JavaScript.

Step 6: Embrace Social Media & UGC

LLMs appear to be heavily dependent on Reddit and Wikipedia. Both sites provide user-generated content on nearly any subject. And with various levels of community-based moderation, a lot of the junk and spam already gets weeded out. Platforms are now dealing with the issue of maintaining a balance between human contributions and the volume of AI-generated content and posts.

Donors can manipulate both platforms, but the general content trustworthiness of those systems is much more customary than it is on the internet. Besides, the two are constantly updated.

Reddit provides valuable insights to LLM labs on how individuals can engage in online discourse, the terminology used in various topics, and knowledge of specific specialty areas.

It is only logical to suppose that user-generated content that is moderated on websites such as Reddit, Wikipedia, Quora, and Stack Overflow will remain relevant in large language models.

I do not suggest spamming those sites, but whenever you have such an opportunity to influence how your brand and competitors are represented there, it might be worth considering.

Step 7: Create For Machine-Readability & Quotability

Write in a manner or style that large language models (LLMs) will understand readily and tend to cite. Although there is nobody who has it right yet, the following approaches appear to work:

Write indicating and factual words. Instead of typing something like “We are fairly confident that the shoe is good on our customers”, type something like this: 96 percent of buyers have self-reported being happy with this shoe.

Add schema. It has been argued many times. Very recently, Fabrice Canel (Principal Product Manager at Bing) stated that schema markup can assist LLMs to comprehend your content.

If you desire to be quoted in an already publicized AI Overview, then you are supposed to possess content that is approximately of the same length as the already available one. Having a high cosine can also assist, but you are not just supposed to copy the current AI Overview. And nerd-fans: yes, you can of course use dot product instead of cosine similarity, given normalization.

In case you use some technical terms that you wish to explain in your content, do so, ideally in a single sentence.

Include summaries of lengthy paragraphs of text, lists of reviews, tables, videos, and other forms of content formats that may be difficult to cite.

Step 8: Optimize your AI-generated content

To be cited for some topics in some LLMs, it helps to:

• • • Add unique words.
    • Have pros/cons.
    • Gather user reviews.
    • Quote experts.
    • Include quantitative data and name your sources.
    • Use easy-to-understand language.
    • Write with positive sentiment.
    • Add product text with low perplexity (predictable and well-structured).
    • Include more lists (like this one!).
    • Provide valuable insights and actionable insights to support data-driven decisions, and ensure human oversight to maintain content quality and accuracy.

Nevertheless, such measures may backfire with other combinations of topics and LLMs.

Until well-established best practices become standard practice, in the short term, my advice is to think about what benefits users, and to experiment enthusiastically.

Step 9: Stick to the Facts

In more than 10 years, algorithms have been able to learn through text represented as triples such as (Subject, Predicate, Object) — in other words, (Lady Liberty, Location, New York). Text that has been disproven by known facts can be considered unreliable, and the information that agrees with the majority opinion and contains new information is the kind that will benefit both LLMs and knowledge graphs.

The facts presented can be validated using mathematical models, and the information provided to LLMs can be precise.

And stick to the ascertained facts. And include some original facts.

Step 10: Invest in Digital PR

All of this will be true of your site as well as literally any other site. How best to influence this? Digital PR!

The more coverage you get for your brand, the more likely LLMs are to repeat it to users.

I have even read situations where advertorials were used as sources!

Concrete GEO Workflows To Try

Before my joining Peec AI, I was a customer. This is how I applied the tool (and how I recommend that our customers apply it). One can simplify the GEO work in the system by using an advanced AI tool or a Gen AI platform and optimize software development.

Learn Who Your Competitors Are

As with other SEOs, leveraging a good GEO tool can reveal unexpected rivals. Review the list of competitors that are identified automatically regularly. In case of any surprises, look into where they have been prompted and investigate the sources that were included in them. Do you figure well in those sources? Otherwise, do something!

Is a competitor cited because of their PeerSpot profile, and you have no reviews in PeerSpot? Make your customers review a product.

Did a popular YouTuber interview the CEO of your competitor? You want to be featured on that show, or create your videos using the exact keywords.

Does your rival often find itself on the top 10 or some lists, whereas you can never break the top 5? It may be a good idea to provide the publisher of the list with a hard-to-resist affiliate offer. You may be the number one with the following content update.

Understand the Sources

Sources are consulted when LLMs carry out generation searching.

Investigate the leading sources that could provide a broad scope of pertinent prompts, but leave your site and rivals on the back street. Of these, you may hear some of the following:

• • • A community like Reddit or X. Become part of the community and join the discussion. X is your best bet to influence results on Grok.
    • An influencer-driven website like YouTube or TikTok. Hire influencers to create videos. Make sure to instruct them to target the right keywords.
    • An affiliate publisher. Buy your way to the top with higher commissions.
    • A news and media publisher. Buy an advertorial and/or target them with your PR efforts. In some instances, you should contact their commercial content department.

Target Query Fanout

Once you have discovered the searches that query fanout is producing on your key prompts, create something with the express intent of targeting those search terms.

Whether that will be on your site, posted on Medium and LinkedIn, published in press releases, or even by paying to place an article–so long as it appears high in search engines, chances are good LLM-based answer engines will cite it.

Position Yourself for AI-Discoverability

Generative Engine Optimization is what is needed most nowadays- it is the vanguard of organic growth. At Peec AI, we are working on tools to help you track, influence, and succeed within this changing landscape.

We have seen clients doubling their LLM-fuelled traffic in 2 to 3 months with conversion rates as much as 20 times higher than conventional SEO traffic!

The question is not whether you are going to do any of the following: create artificial intelligence answers, monitor your brand mentions, or pursue source authority. The question is the urgency in doing so. The LLMs that consumers will use in the future are being trained now.

Conclusion

Generative Engine Optimization is quickly becoming a critical discipline as AI-driven search changes how users discover information. While many traditional SEO best practices still apply, GEO requires a deeper focus on machine readability, source authority, and positioning content where LLMs can find, trust, and quote it. Brands that adopt GEO now can capture early advantages in AI search visibility, potentially driving higher-quality traffic and stronger conversion rates. The future of search is being shaped today, and those who prepare for AI discoverability will be best positioned to lead in the next wave of organic growth.

Generative AI (generative engine) is any artificial intelligence (AI) that can generate content (text, images, audio, video, code, or a combination of many) as opposed to being merely a retriever or classifier of existing content and is deemed to be part of generative AI. Generative AI systems and models are composed of deep learning models, including neural networks, which make them a part of artificial intelligence. Optimization for Generative Engines is a subset of Serch Everywhere Optimization. This is a two part article, where I have separated the basics from the pratical  work.

Highlights

• • Generative AI & LLMs – Generative AI creates new content (text, images, video, audio, code) rather than simply retrieving information. Large Language Models (LLMs) like GPT-4 fall within this category, alongside models using GANs, RNNs, and hybrid neural networks.
  • Generative Engine Optimization (GEO) – A new branch of SEO focused on making content discoverable and quotable by generative AI systems. It emphasizes understanding user intent, providing EEAT-rich content, and optimizing for AI-driven search environments.
  • Two Core Strategies – Influence foundational models (often difficult for most creators) and optimize for Retrieval Augmented Generation (RAG), ensuring your content is chosen as a source and cited often.
  • 10 Practical Steps for GEO –
    1. 1. Ensure your site is crawlable by LLM bots.
       2. Maintain strong traditional SEO rankings.
       3. Target “query fanout” keywords generated by LLMs.
       4. Keep brand mentions consistent across platforms.
       5. Avoid heavy reliance on JavaScript for core content.
       6. Engage on UGC-heavy platforms like Reddit and Wikipedia.
       7. Write in machine-readable, quotable formats with schema and clear facts.
       8. Optimize AI-generated content for unique terms, pros/cons, expert quotes, and structured data.
       9. Stick to verifiable facts and introduce original data.
       10. Invest in digital PR to increase brand authority and citations.
  • Workflow & Tactics – Use GEO tools to identify competitors, analyze high-authority sources, target keywords from query fanouts, and leverage PR, influencer marketing, and affiliate opportunities to secure citations in AI responses.

Genrative AI Vs. Large Language Models

Generative AI allows the creation of content on a diverse variety of outputs, including generated text, image generation (such as realistic images), video generation, music generation, and voice cloning. The use of AI-generated media and content is becoming widespread, enabling the production of content at scale through generative artificial intelligence.

The Generative Engines that only have natural language processing characteristics fall under the large language models (LLMs). These are also a form of foundation models and are instances of advanced models that employ machine learning and neural networks. The generative AI models also employ generative adversarial networks (GANs), recurrent neural networks, and architectures that combine two neural networks. Data augmentation techniques utilize generative AI models to train machine learning models, relying on extensive data sets that contain synthetic and structured data. You can address complex problems in various fields by leveraging outputs from numerous generative AI models that demonstrate high levels of advanced capabilities.

In this article, we will discuss Generative Engine Optimization (GEO), a sub-form of Search Everywhere Optimization (SEO). The initial process of any successful GEO campaign is to generate content that Large Language models will want to link to or cite. To create that content, you need to understand your users’ intent! What is it that the user is interested in finding out, and what is his reason to come seeking answers? Unlike traditional search engines, you are not just optimizing content on sites; you are developing a comprehensive understanding of who, what, where, when, why, and how this content relates to your product or service. The users are not even on your site, and you have to guess the condition that leads them to perform the search.

GEO Strategy Components

Consider experiences that you wouldn’t typically expect to find directly within ChatGPT or similar systems:

• • Engaging content like a 3D tour of the Louvre or a virtual reality concert. Generative AI can also automate the creation of web pages and digital assets, making it easier to deliver interactive and personalized experiences.
  • Live data includes prices, flight delays, and available hotel rooms. While LLMs can integrate this data via APIs, I see the opportunity to capture some of this traffic for the time being.
  • Topics that require EEAT (experience, expertise, authoritativeness, trustworthiness).

Users want a firsthand experience, but LLMs do not have one. Thus, the issue motivates LLMs to cite sources where the knowledge resides and can be accessed firsthand. Well, that is only one critical consideration; what then are the others?

We should differentiate between 2 strategies: the role of influencing the basis of the model and the role of grounding as an instructional tool. Whereas the former is mainly out of reach of most creators, the latter holds opportunities. To succeed, new SEO developments must incorporate improved AI tools to aid content creation and optimization, according to GEO.

Influencing Foundational and Large Language Models

The foundational models have a pre-determined set of data and are not able to learn anything outside their training sets once they are trained. These datasets can incorporate synthetic data, structured data, and data augmentation methods to enhance the performance and robustness of the models. On existing systems such as GPT-4, it is too late – such systems have already been trained.

However, this is relevant towards the future: a so-called refrigerator that is operating on o4-mini in 2025 and which, in theory, may have a preference towards Coke rather than Pepsi. This prejudice may affect purchasing decisions in the future.

Optimizing For Retrieval Augmented Generation (RAG)/Grounding

When large language models (LLMs) are unable to produce answers based solely on their training data, they employ the retrieval augmented generation (RAG) technique to incorporate new information and provide an answer. Such systems as AI Overviews or ChatGPT web search are based on this approach. RAG combines information retrieval and generative model outputs to provide contextually more precise and contextually appropriate answers, resulting in improved contextual knowledge of the system.

As SEO professionals, we want three things:

1. 1. Our content gets selected as a source.
   2. Our content is most frequently quoted within those sources.
   3. Other selected sources support our desired outcome.

Concrete Steps To Succeed With GEO

Don’t panic – there is no rocket science involved in optimizing your content and referencing your brands when using large language models. To the contrary, a lot of the old SEO strategies will still work, and only a couple of new ones will need to be implemented into your routine. AI assistants and AI agents can also be used to automate and simplify your GEO operations, allowing you to streamline content optimization and management processes more easily.

Rewriting How We Find Information Online

Search is no longer about typing keywords and scrolling through blue links. With over 13% of all Google searches now featuring AI Overview responses, nearly doubling from just 6.5% months earlier. We’re witnessing the most significant transformation in how people discover and interact with information online since the birth of the web itself.

Google’s latest search feature is changing the way we find information online. It uses advanced AI technology to make searching easier and more efficient. For marketers, content creators, and business owners, understanding these changes is important for staying visible in this new search landscape. In this easy-to-understand guide, we’ll break down how Gemini Search operates, its impact on your content strategy, and tips on how to adapt to this smarter way of searching.

Highlights

🔍 Search Reinvented with AI

• • Over 13% of Google searches now include AI-generated responses.
  • Google’s Gemini-powered AI Search replaces traditional keyword matching with multi-step reasoning, planning, and multimodal understanding.

🧠 AI Overviews Lead the Experience

• • AI Overviews summarize complex topics right at the top of search results.
  • Users spend more time on the most helpful information and are likelier to engage, driving higher-quality traffic to content creators.

⚙️ Smarter, Personalized, and Multimodal

• • Users can now customize responses, simplify language, or request technical details depending on their needs.
  • Supports text, image, and video inputs.
  • Google’s AI is ideal for planning meal plans, travel itineraries, etc.

🗂️ AI-Organized Results Over Traditional Lists

• • Results are grouped by thematic context, not just ranked.
  • It helps users explore topics faster and more intuitively.

📈 What This Means for Content Creators

• • AI favors original, in-depth, and authoritative content.
  • Clicks from AI Overviews are of higher quality, with more extended time on-site and deeper engagement.

What is Google AI Search

Google Search uses generative AI powered by Google’s Gemini models to go beyond keyword matching. Launched in May 2024, it now serves hundreds of millions of users in the US and will roll out globally throughout 2025.

The system combines multi-step reasoning, planning, and multimodal understanding with Google’s core search infrastructure. This allows the platform to handle complex questions that previously required multiple separate searches and give users comprehensive answers that synthesize information from many web sources.

At its core, the new Google search uses a “query fan-out” technique that breaks down complex queries into subtopics and allows the AI to search multiple sources simultaneously. This gives you richer, more relevant results than traditional sequential searching.

It’s not just an incremental improvement; it’s a complete reimagining of how search can provide answers to complex and straightforward questions.

AI Overviews: The Core Feature

AI Overviews are a key feature of Google search. They create quick, detailed summaries that answer complex questions, so you don’t have to do multiple searches to find information. These AI-generated answers show up at the top of the search results, making it easy for users to get the information they need. Google uses its technology to decide when AI can offer helpful and simplified information for everyone.

The feature excels at handling queries that benefit from information synthesis, such as research topics, how-to questions, or comparison requests. When you search for complex issues, AI Overviews can break down the subject into digestible components while maintaining links to sources.

Enhanced User Engagement

Research shows that AI-generated summaries are much better at keeping users happy and engaged compared to regular search results. In fact, people are 32% more likely to interact with the information they find through AI. They also tend to spend more time looking at the details and asking follow-up questions during their search.

One of the great features of AI responses is that they include direct links to the original websites, which helps bring more visitors to those sites. Interestingly, these links get more clicks than traditional search results, which eases previous worries that websites might lose traffic.

Customization Options

Google is developing customization features that will allow you to tailor AI responses to your needs and understanding. For example, you can turn on controls to simplify language for easier understanding or request more technical detail for expert-level answers.

These controls help you explain topics to different audiences, including kids, making the platform great for educational use cases. Currently available for English searches in the US, these features are the future of personalized search.

Advanced Search

Google AI mode is great at processing complex, multi-layered queries that combine your preferences, location, and specific requirements. It can understand nuanced requests, such as “yoga studios near me with beginner classes and new member discounts.” It understands user intent.

This advanced reasoning goes beyond simple keyword matching. It can understand context, preferences, and conditional requirements in one query.

Multimodal Search

One of the coolest features of Google AI mode is the ability to process video and image inputs along with text queries. You can upload videos to ask questions about objects that can’t describe or troubleshoot issues without knowing the exact technical terms.

This multimodal approach, with Google Lens, supports complex visual queries that combine multiple elements in one searWhecan’tyou’reu’re trying to identify an unknown plant, diagnose a technical issue, or understand how something works, the AI can analyze visual content and give you comprehensive answers.

Planning and Organization

The latest Google search is used for complex planning tasks that require synthesizing information from multiple sources. It can create custom meal planning scenarios and generate 3-day meal plans with recipe suggestions from the web.

It also supports brainstorming sessions, helping you generate new ideas by creating AI-organized result pages grouped under relevant headlines. This transforms search from a simple information retrieval tool into an active planning and creativity partner.

AI-Organized Search Results

Beyond summarizing content, Google’s AI creates new ways to explore topics through thematically organized result pages. Instead of presenting information in traditional ranked lists, AI groups results under intelligent, contextually relevant headings that help users explore different perspectives and content types.

This was first rolled out for dining and recipe searciGoogle’she’sh for US users but will expand to movies, music, books, hotels, shopping, and many other categories. The AI results give fresh angles on familiar topics so users can dive deeper into things that interest them.

These organized results are a fundamental shift from linear search experiences to more intuitive, curated exploration guided by AI’s understanding of content relationships and user intent.

Content Creator Impact and Optimization

Unlike initial concerns about AI reducing website traffic, evidence shows that AI Overviews generate higher-quality engagement. Users who click through from AI-powered responses spend longer on websites and show more engaged browsing behavior than those arriving through traditional search results.

The key is understanding that AI overviews reward content that provides value and unique insights. Generic, commodity content struggles to get visibility, while specialized, authoritative content that satisfies specific user intent gets more exposure.

Best Practices for AI Search Success

To succeed in this new landscape, create original content that adds value beyond what users can find elsewhere. Your content should satisfy AI algorithms and human readers by answering specific questions with comprehensive, well-researched information.

Keep technical optimization fundamentals in place by ensuring Googlebot can access your content and implementing proper structured data markup. Use preview controls like snippet tags when you want to control how your content appears in AI experiences and give yourself granular control over your search presence.

Supporting text content with high-quality images and videos becomes more critical as multimodal search capabilities expand. These visual elements help AI better understand and categorize your content, boosting your visibility in AI results.

Think about the user journey beyond the initial search. Users who land on your site from AI Overview usually want to dive deeper into topics or get specific details. Structure your content to satisfy those deeper information needs while keeping it clear and scannable.

Availability and Access

Gemini Search is available to some users now, with more regions and languages to come in 2024 and 2025. We’re prioritizing quality of experience over speed, so AI features will roll out to everyone.

Search Labs gives you early access to experimental AI mode and additional generative AI features if you want to try out new stuff. This allows us to gather feedback and refine features before they go mainstream.

If you prefer the classic search experience, we have a web filter showing text-based links without AI summaries. This way, you have choice, and we can introduce AI gradually. We’re expanding language support and regional availability, with English in the US having the most features right now.

Privacy and Feedback

Your privacy is very important to Gemini Search. We have strong measures in place to protect your information while using the search tool. We use general, non-personal data to help improve our AI’s performance, so you can benefit from better results without compromising your privacy.

You have control over how your data is used through easy-to-manage settings. This means you can choose whether your searches help our AI get better, allowing you to enjoy the features while still respecting your privacy choices.

We also value your input on the quality of our AI responses. You can easily provide feedback using a simple menu, which helps us keep improving the content we provide. We’re committed to being open and honest, with clear labels showing when content is created by AI. You can always check the sources to verify information and explore topics further through original articles.

The Future of Search is Here

Gemini Search is more than a feature; it fundamentally rethinks how we access and interact with information online. With AI Overviews in over 13% of searches and growing, it’s already here.

For content creators and marketers, this means having a strategy focused on creating valuable, unique content that serves specific user needs. The businesses that will thrive are the ones that understand AI search and optimize their content for it, not the ones clinging to old SEO tactics.

As AI features are rolled out globally and deeper into core search, staying informed of the changes is key to online visibility. The future belongs to those who can adapt their content strategies to work with AI, not against it. Research, project planning, or just looking for something specific? Gemini Search gives you access to what you need. The question is not if AI will change search; how fast will you adapt to use these new powers to improve your visibility?